The goal of this project is to develop tools for using 1H MR echo planar spectroscopic imaging (EPSI) and novel hyperpolarized 13C imaging to improve the management of in patients with glioma. We will tailor metabolic acquisitions for the specific molecular markers, such as 1p19q co-deletion, IDH mutation status and TERT promotor mutation (TERTp), which have been recently incorporated into the new WHO 2016 classification. In the previous cycle of this P01, we have identified several molecular markers to differentiate IDH mutation status and predict malignant progression based on ex vivo 1H HRMAS-NMR spectroscopy from image-guided tissue samples and in vivo 1H EPSI, and we have also developed, implemented and optimized acquisitions, post- processing and quantification of 1H EPSI and hyperpolarized [1-13C]pyruvate metabolic imaging for patients with glioma. In this proposed project, we will expand upon sequences used for 1H metabolic imaging and upon the agents used for hyperpolarized 13C imaging in order to determine which markers are the most relevant for evaluating patients from different molecular subgroups. In Aim 1 we will develop an integrated protocol that combines 1H lactate edited EPSI and [1-13C]pyruvate for patients with IDH-negative TERTp+ glioblastoma, and investigate the impact of treatment with RT and temozolomide on multiple metabolites, as well as their association with outcome. In Aim 2 we will evaluate metabolic changes during treatment with RT and temozolomide for patients with IDH+ astrocytoma in order to determine markers for early decision making by using 1H methods to detect 2-hydroxyglutarate (2HG) and hyperpolarized [2-13C]pyruvate metabolic imaging to detect differences in glutamate. In Aim 3 we will explore whether estimates of 2HG from 1H spectra or estimates obtained using hyperpolarized [1-13C]?-ketoglutarate metabolic imaging are more sensitive for identifying regions of malignant progression in recurrent tumor for patients with IDH+, 1p/19q co-deleted and TERTp+ oligodendroglioma. The results of the project will identify metabolic parameters that are indicative of early treatment effectiveness for patients from different molecular sub-groups and will enable rapid selection of the most appropriate therapies.